New Details on Reverse RL Goal Inference: Factor in Goal Difficulty
ben_eysenbach · x · 2026-07-07
RL researcher Ben Eysenbach points out an easily overlooked detail: inferring an expert's goal using inverse reinforcement learning isn't just about picking the states the expert visits most frequently or last. For instance, the runner-up in a race was likely also trying their best to win. He notes that previous zero-shot imitation methods often made this mistake, and the fix is to factor in the difficulty of achieving each goal during the inference process.
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